Hybrid sparse-representation-based approach to image super-resolution reconstruction

Di Zhang; Jiazhong He

doi:10.1117/1.JEI.26.2.023008

21 March 2017 Hybrid sparse-representation-based approach to image super-resolution reconstruction

Di Zhang, Jiazhong He

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 2, 023008 (March 2017). https://doi.org/10.1117/1.JEI.26.2.023008

Abstract

This paper presents a hybrid sparse-representation-based approach to single-image super-resolution reconstruction. Our main contribution is threefold: (1) jointly utilize nonlocal similarity of intensity image and low-rank property of gradient image under the framework of sparse representation; (2) incorporate both the high-resolution (HR) and low-resolution dictionaries into the reconstruction process; and (3) incorporate both the unknown HR image and the sparse coefficients into a single objective function. By alternatively minimizing the objective function with respect to the unknown HR image and the sparse coefficients, we get an estimate of the target HR image. Extensive experiments validate that compared with many state-of-the-art algorithms the proposed method yields comparable results for noiseless images and achieves much better results for noisy images.

Citation Download Citation

Di Zhang and Jiazhong He "Hybrid sparse-representation-based approach to image super-resolution reconstruction," Journal of Electronic Imaging 26(2), 023008 (21 March 2017). https://doi.org/10.1117/1.JEI.26.2.023008

Received: 19 August 2016; Accepted: 1 March 2017; Published: 21 March 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available